Blog list grid Type 2
Processing huge volume of data on a day to day basis is a tedious, time-consuming affair for insurance companies. AI could just be the game changer due to its speed and utility.
Intelligent Automation(IA) systems can detect and produce large amounts of information, automate an entire process or workflow, learn and adapt as it keeps improving with daily updates and interaction.
Cognitive AI uses previously under-utilized data for segmentation, identification, and scoring of customers. This is the edge a data-driven customer experience has over others.
Automation in logistics is at a nascent stage. Reports show that implementing automation can deliver up to 30% increase in transaction speed, 25% reduction in cost, 100% accuracy and better compliance.
Banking and financial sectors have been using some form of machine learning to keep track of data but it is usually tedious and manual in nature. With high volume of data and the quantitative nature of finance, this sector is particularly suited for artificial intelligence.
RPA is clearly considered an improvement over assisted automation but still, there are places where it is more suitable than unassisted automation. Myths and misunderstandings around robotic process automation however are still many.
A McKinsey study says that Robotic Process Automation (RPA) is a promising new development in business automation that offers a potential ROI of 30–200 percent — in the first year.
Wealth management advice will come from bots that are free of calculation errors and faulty/biased judgement, unlike human advisors. AI and machine learning will play a key role in solving portfolio advice and equipping experts with key data points.